For any task or occupation, sleep deprivation can result in the decline of alertness levels, thereby resulting in the degradation of work performance. To this end, sleeping may be considered an effective countermeasure to prevent decline in alertness levels. A possible drawback of a sleep period may be sleep inertia, which is the feeling of grogginess or post awakening performance degradation. The recovery effect of sleep may depend on various factors related to sleep including sleep duration and wake time, which may determine sleep inertia. Furthermore, the positive effects of sleeping can depend on, various factors such as the duration of sleep, the sleep-wake history, and the stage of sleep from which a person awakens. Depending upon the user allotted time for sleep, including sleep start time and sleep duration, an estimation of the optimum wake time may allow a user to resume his/her work at an optimal performance level with minimal post awakening performance degradation.
Though sleep patterns may vary, it is generally accepted that individuals pass through various stages during sleep, called sleep stages. Typically, sleep may be categorized into either Rapid Eye Movement (REM) sleep or non-REM (NREM) sleep. NREM sleep may be associated with four sleep stages, with sleep stages 1 and 2 corresponding to lighter sleep and sleep stages 3 and 4 corresponding to deeper sleep. Generally, sleep inertia is a function of increasing sleep stage. Thus, waking a person from a lighter stage of sleep, such as stage 1, may result in less sleep inertia than waking a person from a deeper stage of sleep.
Literature has suggested that the power of the delta wave (“delta power”), as measured by an electroencephalogram (EEG), may correspond to sleep depth as indicated by one of the various sleep stages. A delta wave is a type of brain wave with a specific frequency range. Delta power decreases in REM sleep and increases through NREM sleep stages 1, with the highest delta power typically being associated with NREM stage 4. However, even within a sleep stage, delta power will vary. Delta power also varies based on the duration of sleep and sleep-wake history. For example, an estimated optimum wake time may differ for a sleep duration of forty minutes as compared to a sleep duration of eight hours.
In some instances, only a short period of time is allocated for a sleep period. Therefore, as sleeping may provide significant improvements in alertness levels, measuring delta power to determine the ideal wake time to afford minimum sleep inertia may be important to maximizing the recovery effects of sleeping. While a number of biological alarms that monitor sleep patterns of a user are available on the market, these alarms are primarily concerned with reducing the effects of sleep inertia associated with waking up from night time sleep period. Some products, for example, focus on scanning for a user's light stages of sleep (Stages 1 and 2) in the sleep cycle and waking up the user during or near these moments or near the user's scheduled wake time. In effect, these products use bodily activity patterns to attempt to estimate the sleep stage in order to wake a person during the lightest sleep stage. Since there is very little bodily activity in deeper stages of sleep, current products fail to recommend wake times in those stages. Further, current products do not take into consideration variability of distinct frequency bands, such as ones indicating delta power within each sleep stage and assume that as a user transitions from light sleep to deep sleep, there is a steady rise in delta power. Thus, current products do not recommend wake times in deep stages of sleep as they presume this may result in higher sleep inertia. Additionally, few current designs offer the ability to collect brain activity patterns, such as delta waves, from a user. Without the ability to collect brain activity data, current designs do not offer the ability to adapt the optimal wake time selection to match a user's brain activity, and fail to recommend optimal wake times within a sleep cycle. Products that incorporate the collection of brain activity patterns may use the data to determine a transition into or out of REM sleep, and wake the user at the transition point. These products assume that sleep inertia is at a maximum during deep stages of sleep, and again fail to consider the variability of delta power within a sleep stage.
Thus, a need exists for systems and methods for providing optimum wake time(s) based on fluctuations in brain activity frequencies to minimize sleep inertia. Such systems and methods monitor and/or determine sleep conditions which may improve alertness such as by maximizing sleep duration, minimizing sleep inertia after awakening, and/or minimizing wake inertia as users prepare to fall asleep. In addition, such systems and methods may determine optimum wake time(s) within a single sleep stage as well as between successive sleep stages. Further, by offering the ability to directly collect brain activity patterns from a user, systems and methods disclosed herein may determine optimum wake time(s) based on a user's ongoing brain activity.